Multi‐dimensional Taylor Network‐based Control for a Class of Nonlinear Stochastic Systems with Full State Time‐varying Constraints and the Finite‐time Output Constraint
摘要
In this paper, the adaptive multi-dimensional Taylor network (MTN) control problem is investigated for nonlinear stochastic systems with full state time-ying constraints and the finite-time output constraint. By combining the MTN-based approximation method and the adaptive backstepping control method, a novel adaptive MTN control scheme is provided by constructing the time-ying barrier Lyapunov function (TVBLF). To implement the finite-time output constraint, the finite-time performance function (FTPF) is introduced in the control scheme. The proposed scheme can ensure that the tracking error finally converges to a small neighborhood of the origin in the finite-time and all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) in probability. Finally, two simulation examples are presented to show the effectiveness of the provided control scheme.
类型
出版物
Asian Journal of Control
Adaptive Neural-Control
Barrier Lyapunov Functions
Control Design
Input
Stabilization
Strict-Feedback Systems
Tracking Control
Authors
Authors
正教授
博士，教授，硕士生导师，人工智能技术海洋场景化应用山东省工程研究中心副主任，青岛市人工智能海洋技术创新中心副主任，青岛科技大学数学与交叉研究院副院长。山东赛区数学建模竞赛专家组成员、山东省数学会理事、山东省应用统计学会理事、人工智能海洋学专业委员会委员。近年来，主持或参与国家自然科学基金、省自然基金、省教改项目等各类教学科研项目20多项，在国内外期刊发表学术论文80余篇，其中被SCI、EI检索70余篇，参编教材1部。指导学生参加全国大学生数学建模竞赛、中国研究生数学建模竞赛、美国大学生数学建模竞赛等各类竞赛获国家一等奖9项、国家二等奖29项、国家三等奖13项、山东省一等奖37项、山东省二等奖12项、山东省三等奖7项。指导本科生参加国家大学生创新计划项目4项。
Authors